Abstract
The problem of imbalanced data, i.e., when the class labels are unequally distributed, is encountered in many real-life application, e.g., credit scoring, medical diagnostics. Various approaches aimed at dealing with the imbalanced data have been proposed. One of the most well known data pre-processing method is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE may generate examples which are artificial in the sense that they are impossible to be drawn from the true distribution. Therefore, in this paper, we propose to apply Restricted Boltzmann Machine to learn an intermediate representation which transform the SMOTE examples to the ones approximately drawn from the true distribution. At the end of the paper we perform an experiment using credit scoring dataset.
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Zięba, M., Tomczak, J.M., Gonczarek, A. (2015). RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_37
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DOI: https://doi.org/10.1007/978-3-319-15702-3_37
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